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  <div class="section" id="numpy-take">
<h1>numpy.take<a class="headerlink" href="#numpy-take" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="numpy.take">
<code class="sig-prename descclassname">numpy.</code><code class="sig-name descname">take</code><span class="sig-paren">(</span><em class="sig-param">a</em>, <em class="sig-param">indices</em>, <em class="sig-param">axis=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">mode='raise'</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/numpy/numpy/blob/v1.18.1/numpy/core/fromnumeric.py#L97-L194"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#numpy.take" title="Permalink to this definition">¶</a></dt>
<dd><p>Take elements from an array along an axis.</p>
<p>When axis is not None, this function does the same thing as “fancy”
indexing (indexing arrays using arrays); however, it can be easier to use
if you need elements along a given axis. A call such as
<code class="docutils literal notranslate"><span class="pre">np.take(arr,</span> <span class="pre">indices,</span> <span class="pre">axis=3)</span></code> is equivalent to
<code class="docutils literal notranslate"><span class="pre">arr[:,:,:,indices,...]</span></code>.</p>
<p>Explained without fancy indexing, this is equivalent to the following use
of <a class="reference internal" href="numpy.ndindex.html#numpy.ndindex" title="numpy.ndindex"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndindex</span></code></a>, which sets each of <code class="docutils literal notranslate"><span class="pre">ii</span></code>, <code class="docutils literal notranslate"><span class="pre">jj</span></code>, and <code class="docutils literal notranslate"><span class="pre">kk</span></code> to a tuple of
indices:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Ni</span><span class="p">,</span> <span class="n">Nk</span> <span class="o">=</span> <span class="n">a</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="n">axis</span><span class="p">],</span> <span class="n">a</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="n">axis</span><span class="o">+</span><span class="mi">1</span><span class="p">:]</span>
<span class="n">Nj</span> <span class="o">=</span> <span class="n">indices</span><span class="o">.</span><span class="n">shape</span>
<span class="k">for</span> <span class="n">ii</span> <span class="ow">in</span> <span class="n">ndindex</span><span class="p">(</span><span class="n">Ni</span><span class="p">):</span>
    <span class="k">for</span> <span class="n">jj</span> <span class="ow">in</span> <span class="n">ndindex</span><span class="p">(</span><span class="n">Nj</span><span class="p">):</span>
        <span class="k">for</span> <span class="n">kk</span> <span class="ow">in</span> <span class="n">ndindex</span><span class="p">(</span><span class="n">Nk</span><span class="p">):</span>
            <span class="n">out</span><span class="p">[</span><span class="n">ii</span> <span class="o">+</span> <span class="n">jj</span> <span class="o">+</span> <span class="n">kk</span><span class="p">]</span> <span class="o">=</span> <span class="n">a</span><span class="p">[</span><span class="n">ii</span> <span class="o">+</span> <span class="p">(</span><span class="n">indices</span><span class="p">[</span><span class="n">jj</span><span class="p">],)</span> <span class="o">+</span> <span class="n">kk</span><span class="p">]</span>
</pre></div>
</div>
<dl class="field-list">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl>
<dt><strong>a</strong><span class="classifier">array_like (Ni…, M, Nk…)</span></dt><dd><p>The source array.</p>
</dd>
<dt><strong>indices</strong><span class="classifier">array_like (Nj…)</span></dt><dd><p>The indices of the values to extract.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 1.8.0.</span></p>
</div>
<p>Also allow scalars for indices.</p>
</dd>
<dt><strong>axis</strong><span class="classifier">int, optional</span></dt><dd><p>The axis over which to select values. By default, the flattened
input array is used.</p>
</dd>
<dt><strong>out</strong><span class="classifier">ndarray, optional (Ni…, Nj…, Nk…)</span></dt><dd><p>If provided, the result will be placed in this array. It should
be of the appropriate shape and dtype. Note that <em class="xref py py-obj">out</em> is always
buffered if <em class="xref py py-obj">mode=’raise’</em>; use other modes for better performance.</p>
</dd>
<dt><strong>mode</strong><span class="classifier">{‘raise’, ‘wrap’, ‘clip’}, optional</span></dt><dd><p>Specifies how out-of-bounds indices will behave.</p>
<ul class="simple">
<li><p>‘raise’ – raise an error (default)</p></li>
<li><p>‘wrap’ – wrap around</p></li>
<li><p>‘clip’ – clip to the range</p></li>
</ul>
<p>‘clip’ mode means that all indices that are too large are replaced
by the index that addresses the last element along that axis. Note
that this disables indexing with negative numbers.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>out</strong><span class="classifier">ndarray (Ni…, Nj…, Nk…)</span></dt><dd><p>The returned array has the same type as <em class="xref py py-obj">a</em>.</p>
</dd>
</dl>
</dd>
</dl>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<dl class="simple">
<dt><a class="reference internal" href="numpy.compress.html#numpy.compress" title="numpy.compress"><code class="xref py py-obj docutils literal notranslate"><span class="pre">compress</span></code></a></dt><dd><p>Take elements using a boolean mask</p>
</dd>
<dt><a class="reference internal" href="numpy.ndarray.take.html#numpy.ndarray.take" title="numpy.ndarray.take"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.take</span></code></a></dt><dd><p>equivalent method</p>
</dd>
<dt><a class="reference internal" href="numpy.take_along_axis.html#numpy.take_along_axis" title="numpy.take_along_axis"><code class="xref py py-obj docutils literal notranslate"><span class="pre">take_along_axis</span></code></a></dt><dd><p>Take elements by matching the array and the index arrays</p>
</dd>
</dl>
</div>
<p class="rubric">Notes</p>
<p>By eliminating the inner loop in the description above, and using <a class="reference internal" href="numpy.s_.html#numpy.s_" title="numpy.s_"><code class="xref py py-obj docutils literal notranslate"><span class="pre">s_</span></code></a> to
build simple slice objects, <a class="reference internal" href="#numpy.take" title="numpy.take"><code class="xref py py-obj docutils literal notranslate"><span class="pre">take</span></code></a> can be expressed  in terms of applying
fancy indexing to each 1-d slice:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Ni</span><span class="p">,</span> <span class="n">Nk</span> <span class="o">=</span> <span class="n">a</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="n">axis</span><span class="p">],</span> <span class="n">a</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="n">axis</span><span class="o">+</span><span class="mi">1</span><span class="p">:]</span>
<span class="k">for</span> <span class="n">ii</span> <span class="ow">in</span> <span class="n">ndindex</span><span class="p">(</span><span class="n">Ni</span><span class="p">):</span>
    <span class="k">for</span> <span class="n">kk</span> <span class="ow">in</span> <span class="n">ndindex</span><span class="p">(</span><span class="n">Nj</span><span class="p">):</span>
        <span class="n">out</span><span class="p">[</span><span class="n">ii</span> <span class="o">+</span> <span class="n">s_</span><span class="p">[</span><span class="o">...</span><span class="p">,]</span> <span class="o">+</span> <span class="n">kk</span><span class="p">]</span> <span class="o">=</span> <span class="n">a</span><span class="p">[</span><span class="n">ii</span> <span class="o">+</span> <span class="n">s_</span><span class="p">[:,]</span> <span class="o">+</span> <span class="n">kk</span><span class="p">][</span><span class="n">indices</span><span class="p">]</span>
</pre></div>
</div>
<p>For this reason, it is equivalent to (but faster than) the following use
of <a class="reference internal" href="numpy.apply_along_axis.html#numpy.apply_along_axis" title="numpy.apply_along_axis"><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply_along_axis</span></code></a>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">out</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">apply_along_axis</span><span class="p">(</span><span class="k">lambda</span> <span class="n">a_1d</span><span class="p">:</span> <span class="n">a_1d</span><span class="p">[</span><span class="n">indices</span><span class="p">],</span> <span class="n">axis</span><span class="p">,</span> <span class="n">a</span><span class="p">)</span>
</pre></div>
</div>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">a</span> <span class="o">=</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">8</span><span class="p">]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">indices</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">take</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">indices</span><span class="p">)</span>
<span class="go">array([4, 3, 6])</span>
</pre></div>
</div>
<p>In this example if <em class="xref py py-obj">a</em> is an ndarray, “fancy” indexing can be used.</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">a</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">a</span><span class="p">[</span><span class="n">indices</span><span class="p">]</span>
<span class="go">array([4, 3, 6])</span>
</pre></div>
</div>
<p>If <a class="reference internal" href="numpy.indices.html#numpy.indices" title="numpy.indices"><code class="xref py py-obj docutils literal notranslate"><span class="pre">indices</span></code></a> is not one dimensional, the output also has these dimensions.</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">take</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]])</span>
<span class="go">array([[4, 3],</span>
<span class="go">       [5, 7]])</span>
</pre></div>
</div>
</dd></dl>

</div>


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